{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.750570Z",
     "start_time": "2025-03-03T04:33:46.183030Z"
    }
   },
   "source": [
    "import pandas as pd"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 Series"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# 生成一个Series\n",
    "\n",
    "ser_obj = pd.Series(range(10, 20))  #默认索引是0-9 数据是range(10,20)  \n",
    "print(ser_obj)  #打印输出会带有类型\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.757572Z",
     "start_time": "2025-03-03T04:33:46.752578Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10\n",
      "1    11\n",
      "2    12\n",
      "3    13\n",
      "4    14\n",
      "5    15\n",
      "6    16\n",
      "7    17\n",
      "8    18\n",
      "9    19\n",
      "dtype: int64\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.770169Z",
     "start_time": "2025-03-03T04:33:46.757572Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print('-' * 50)\n",
    "# 获取数据\n",
    "print(ser_obj.values)  #values实际是ndarray\n",
    "print(type(ser_obj.values))  #类型是ndarray\n",
    "# 获取索引\n",
    "print(ser_obj.index)  #内部自带的类型--RangeIndex\n",
    "ser_obj.dtype  #数据类型"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "[10 11 12 13 14 15 16 17 18 19]\n",
      "<class 'numpy.ndarray'>\n",
      "RangeIndex(start=0, stop=10, step=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "print(ser_obj[0])\n",
    "ser_obj[9]  #\n",
    "# 访问不存在的索引下标会报keyerror"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.776928Z",
     "start_time": "2025-03-03T04:33:46.771175Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.int64(19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "source": [
    "print(ser_obj * 2)  #元素级乘法\n",
    "print(ser_obj > 15)  #返回一个bool序列"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.785373Z",
     "start_time": "2025-03-03T04:33:46.778930Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    20\n",
      "1    22\n",
      "2    24\n",
      "3    26\n",
      "4    28\n",
      "5    30\n",
      "6    32\n",
      "7    34\n",
      "8    36\n",
      "9    38\n",
      "dtype: int64\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "5    False\n",
      "6     True\n",
      "7     True\n",
      "8     True\n",
      "9     True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "source": [
    "#字典变为series，索引是字典的key，value是字典的value，感受非默认索引\n",
    "\n",
    "year_data = {2001: 17.8, 2005: 20.1, 2003: 16.5}\n",
    "ser_obj2 = pd.Series(year_data)\n",
    "print(ser_obj2)\n",
    "print('-' * 50)\n",
    "print(ser_obj2.index)\n",
    "print('-' * 50)\n",
    "print(ser_obj2[2001])\n",
    "ser_obj2.values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.793093Z",
     "start_time": "2025-03-03T04:33:46.785373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001    17.8\n",
      "2005    20.1\n",
      "2003    16.5\n",
      "dtype: float64\n",
      "--------------------------------------------------\n",
      "Index([2001, 2005, 2003], dtype='int64')\n",
      "--------------------------------------------------\n",
      "17.8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([17.8, 20.1, 16.5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "source": [
    "#有点鸡肋\n",
    "print(ser_obj2.name)  #Series名字\n",
    "print(ser_obj2.index.name)  #索引名字\n",
    "ser_obj2.name = 'temp'\n",
    "ser_obj2.index.name = 'year1'\n",
    "print('-' * 50)\n",
    "print(ser_obj2.head())  #head默认显示前5行\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.800310Z",
     "start_time": "2025-03-03T04:33:46.794104Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n",
      "--------------------------------------------------\n",
      "year1\n",
      "2001    17.8\n",
      "2005    20.1\n",
      "2003    16.5\n",
      "Name: temp, dtype: float64\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 3 DataFrame"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "# 通过ndarray构建DataFrame\n",
    "t = pd.DataFrame(np.arange(12).reshape((3, 4)))  #默认索引是0-2\n",
    "print(t)\n",
    "print('-' * 50)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.808592Z",
     "start_time": "2025-03-03T04:33:46.801313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1   2   3\n",
      "0  0  1   2   3\n",
      "1  4  5   6   7\n",
      "2  8  9  10  11\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.815773Z",
     "start_time": "2025-03-03T04:33:46.808592Z"
    }
   },
   "cell_type": "code",
   "source": [
    "array = np.random.randn(5, 4)\n",
    "print(array)\n",
    "print('-' * 50)\n",
    "df_obj = pd.DataFrame(array)\n",
    "print(df_obj.head())  #  head拿前几行，默认显示前5行"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.08069081 -0.83522323 -0.04866179  0.12029742]\n",
      " [ 0.28766747 -0.34370923  1.7729012   0.73103392]\n",
      " [ 1.70325081  1.55190361  2.03927177 -0.16562616]\n",
      " [-0.52968992 -1.58673108 -0.40817186 -0.72420817]\n",
      " [-0.34214318  0.50474705 -0.46888335 -1.05345035]]\n",
      "--------------------------------------------------\n",
      "          0         1         2         3\n",
      "0  0.080691 -0.835223 -0.048662  0.120297\n",
      "1  0.287667 -0.343709  1.772901  0.731034\n",
      "2  1.703251  1.551904  2.039272 -0.165626\n",
      "3 -0.529690 -1.586731 -0.408172 -0.724208\n",
      "4 -0.342143  0.504747 -0.468883 -1.053450\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": "t.loc[0]  #单独把某一行取出来,类型是series",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.820809Z",
     "start_time": "2025-03-03T04:33:46.816769Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "source": [
    "# 列表套字典  变df\n",
    "d2 = [{\"name\": \"xiaohong\", \"age\": 32, \"tel\": 10010},\n",
    "      {\"name\": \"xiaogang\", \"tel\": 10000},\n",
    "      {\"name\": \"xiaowang\", \"age\": 22}]\n",
    "df6 = pd.DataFrame(d2)\n",
    "print(df6)  #缺失值会用NaN填充\n",
    "print(type(df6.values))  #ndarray"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.828849Z",
     "start_time": "2025-03-03T04:33:46.820809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       name   age      tel\n",
      "0  xiaohong  32.0  10010.0\n",
      "1  xiaogang   NaN  10000.0\n",
      "2  xiaowang  22.0      NaN\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "source": "pd.Series(1, index=list(range(3, 7)), dtype='float32')",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.836632Z",
     "start_time": "2025-03-03T04:33:46.829853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1.0\n",
       "4    1.0\n",
       "5    1.0\n",
       "6    1.0\n",
       "dtype: float32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "source": [
    "#df中不同列可以是不同的数据类型,同一列必须是一个数据类型\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dict_data = {'A': 1,\n",
    "             'B': pd.Timestamp('20190926'),\n",
    "             'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "             'D': np.array([1, 2, 3, 4], dtype='int32'),\n",
    "             'E': [\"Python\", \"Java\", \"C++\", \"C\"],\n",
    "             'F': 'wangdao'}\n",
    "df_obj2 = pd.DataFrame(dict_data)\n",
    "print(df_obj2)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.845421Z",
     "start_time": "2025-03-03T04:33:46.837634Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A          B    C  D       E        F\n",
      "0  1 2019-09-26  1.0  1  Python  wangdao\n",
      "1  1 2019-09-26  1.0  2    Java  wangdao\n",
      "2  1 2019-09-26  1.0  3     C++  wangdao\n",
      "3  1 2019-09-26  1.0  4       C  wangdao\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.853092Z",
     "start_time": "2025-03-03T04:33:46.846424Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print('-' * 50)\n",
    "print(df_obj2.index)  #行索引,重点\n",
    "#补课改变\n",
    "# df_obj2.index[0]=2  不可以单独修改某个索引值\n",
    "print(df_obj2.columns)  #列索引，重点\n",
    "df_obj2.dtypes  #每一列的数据类型，重点"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Index([0, 1, 2, 3], dtype='int64')\n",
      "Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "A            int64\n",
       "B    datetime64[s]\n",
       "C          float32\n",
       "D            int32\n",
       "E           object\n",
       "F           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "source": [
    "# 感受日期,初始化df，设置行索引，列索引\n",
    "dates = pd.date_range('20130101', periods=6)  #默认freq='D'，即天\n",
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))\n",
    "print(df)\n",
    "print('-' * 50)\n",
    "print(df.index)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.863606Z",
     "start_time": "2025-03-03T04:33:46.853092Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-01 -1.543235  0.548775  0.618148 -1.699463\n",
      "2013-01-02  0.018435  0.515621  1.221951  0.557005\n",
      "2013-01-03 -0.153180 -0.326635  0.419559  0.467091\n",
      "2013-01-04  0.810186  1.439480 -0.032337  0.337390\n",
      "2013-01-05 -0.331142 -1.463407  0.564485 -0.592197\n",
      "2013-01-06  1.672722  0.467464  0.225605  1.075973\n",
      "--------------------------------------------------\n",
      "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
      "               '2013-01-05', '2013-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "source": [
    "#取数据\n",
    "print(df_obj2)\n",
    "print('-' * 50)\n",
    "print(type(df_obj2))\n",
    "print('-' * 50)\n",
    "#pd中使用索引名来取某一行，或者列\n",
    "print(df_obj2['B'])\n",
    "print('-' * 50)\n",
    "#把df的某一列取出来是series\n",
    "print(type(df_obj2['B']))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.870089Z",
     "start_time": "2025-03-03T04:33:46.863606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A          B    C  D       E        F\n",
      "0  1 2019-09-26  1.0  1  Python  wangdao\n",
      "1  1 2019-09-26  1.0  2    Java  wangdao\n",
      "2  1 2019-09-26  1.0  3     C++  wangdao\n",
      "3  1 2019-09-26  1.0  4       C  wangdao\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "--------------------------------------------------\n",
      "0   2019-09-26\n",
      "1   2019-09-26\n",
      "2   2019-09-26\n",
      "3   2019-09-26\n",
      "Name: B, dtype: datetime64[s]\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "source": [
    "#增加列数据，列名是自定义的\n",
    "df_obj2['G'] = df_obj2['D'] + 4\n",
    "print(df_obj2.head())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.879692Z",
     "start_time": "2025-03-03T04:33:46.870089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A          B    C  D       E        F  G\n",
      "0  1 2019-09-26  1.0  1  Python  wangdao  5\n",
      "1  1 2019-09-26  1.0  2    Java  wangdao  6\n",
      "2  1 2019-09-26  1.0  3     C++  wangdao  7\n",
      "3  1 2019-09-26  1.0  4       C  wangdao  8\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": [
    "# 删除列\n",
    "del (df_obj2['G'])\n",
    "print(df_obj2.head())\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-03-03T04:33:46.888109Z",
     "start_time": "2025-03-03T04:33:46.880704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A          B    C  D       E        F\n",
      "0  1 2019-09-26  1.0  1  Python  wangdao\n",
      "1  1 2019-09-26  1.0  2    Java  wangdao\n",
      "2  1 2019-09-26  1.0  3     C++  wangdao\n",
      "3  1 2019-09-26  1.0  4       C  wangdao\n"
     ]
    }
   ],
   "execution_count": 18
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
